Background:

Cluster-randomized trials allow for the evaluation of a community-level or group-/cluster-level intervention. For studies that require a cluster-randomized trial design to evaluate cluster-level interventions aimed at controlling vector-borne diseases, it may be difficult to assess a large number of clusters while performing the additional work needed to monitor participants, vectors, and environmental factors associated with the disease. One such example of a cluster-randomized trial with few clusters was the “efficacy and risk of harms of repeated ivermectin mass drug administrations for control of malaria” trial. Although previous work has provided recommendations for analyzing trials like repeated ivermectin mass drug administrations for control of malaria, additional evaluation of the multiple approaches for analysis is needed for study designs with count outcomes.

Methods:

Using a simulation study, we applied three analysis frameworks to three cluster-randomized trial designs (single-year, 2-year parallel, and 2-year crossover) in the context of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria. Mixed-effects models, generalized estimating equations, and cluster-level analyses were evaluated. Additional 2-year parallel designs with different numbers of clusters and different cluster correlations were also explored.

Results:

Mixed-effects models with a small sample correction and unweighted cluster-level summaries yielded both high power and control of the Type I error rate. Generalized estimating equation approaches that utilized small sample corrections controlled the Type I error rate but did not confer greater power when compared to a mixed model approach with small sample correction. The crossover design generally yielded higher power relative to the parallel equivalent. Differences in power between analysis methods became less pronounced as the number of clusters increased. The strength of within-cluster correlation impacted the relative differences in power.

Conclusion:

Regardless of study design, cluster-level analyses as well as individual-level analyses like mixed-effects models or generalized estimating equations with small sample size corrections can both provide reliable results in small cluster settings. For 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria, we recommend a mixed-effects model with a pseudo-likelihood approximation method and Kenward–Roger correction. Similarly designed studies with small sample sizes and count outcomes should consider adjustments for small sample sizes when using a mixed-effects model or generalized estimating equation for analysis. Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. Finally, the sensitivity of the analysis approach to the strength of within-cluster correlation should be carefully considered when selecting the primary analysis for a cluster-randomized trial.

中文翻译：

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用于控制疟疾的重复伊维菌素大规模给药的 2 年平行随访的设计和分析：具有计数数据的集群随机试验的小样本考虑

背景：

集群随机试验允许评估社区级别或团体/集群级别的干预。对于需要集群随机试验设计来评估旨在控制病媒传播疾病的集群级别干预措施的研究，在执行监测参与者、载体和环境因素所需的额外工作时，可能难以评估大量集群与疾病有关。集群随机试验的一个这样的例子是“重复伊维菌素大规模给药控制疟疾的疗效和危害风险”试验。尽管以前的工作已经为分析试验提供了建议，例如重复伊维菌素大规模药物管理以控制疟疾，

方法：

使用模拟研究，我们将三个分析框架应用于三个集群随机试验设计（单年、2 年平行和 2 年交叉），在重复伊维菌素大规模药物 2 年平行随访的背景下控制疟疾的行政部门。评估了混合效应模型、广义估计方程和集群级分析。还探索了具有不同集群数量和不同集群相关性的其他 2 年并行设计。

结果：

具有小样本校正和未加权集群级摘要的混合效应模型产生了高功率和 I 类错误率的控制。利用小样本校正的广义估计方程方法控制了 I 类错误率，但与具有小样本校正的混合模型方法相比，并没有提供更大的功效。相对于并联等效物，交叉设计通常产生更高的功率。随着聚类数量的增加，分析方法之间的功效差异变得不那么明显。集群内相关性的强度影响了权力的相对差异。

结论：

无论研究设计如何，集群级分析以及混合效应模型或具有小样本量校正的广义估计方程等个体级分析都可以在小型集群设置中提供可靠的结果。对于 2 年的重复伊维菌素大规模给药以控制疟疾的平行随访，我们推荐使用伪似然近似法和 Kenward-Roger 校正的混合效应模型。在使用混合效应模型或广义估计方程进行分析时，具有小样本量和计数结果的类似设计的研究应考虑对小样本量进行调整。虽然重复伊维菌素大规模药物管理以控制疟疾的 2 年平行随访已经作为平行试验进行，将仿真参数应用于交叉设计可以提高功率，这表明交叉设计在可用集群数量有限的环境中可能很有价值。最后，在为集群随机试验选择主要分析时，应仔细考虑分析方法对集群内相关性强度的敏感性。